Data Handling
Data residency
Section titled “Data residency”All data processing happens inside the consumer’s environment. On Snowflake, this means the consumer’s Snowflake account. On Databricks, the consumer’s workspace. No data is sent to Integrated Quantum Technologies or any external service.
What the app accesses
Section titled “What the app accesses”| Data | Access | Purpose |
|---|---|---|
| Feature columns (training) | SELECT via warehouse | Read consumer-specified columns to train encoder |
| Feature columns (inference) | Masking policy intercept | Encode values at query time |
| Model artifacts | Internal stage read/write | Store and load ONNX encoder files |
| Encoder registry | App-owned tables | Track encoder metadata, training jobs, configuration |
What the app does not access
Section titled “What the app does not access”- No consumer credentials are requested or accepted
- No data is sent to the provider account
- No data is sent to external APIs or services
- No telemetry, analytics, or usage data leaves the consumer environment (billing events are emitted to Snowflake’s billing system, not to the provider)
Encoding properties
Section titled “Encoding properties”Non-invertible. The encoder-only half of the autoencoder is exported. The decoder is discarded after training. Exact reconstruction of raw input from the latent vector is not possible.
Task-relevant information. If the latent vector is useful for ML, it contains information correlated with the task. When sensitive attributes correlate with the task, partial information about those attributes is present in the encoding. Non-invertible does not mean non-informative.
Deterministic. The same input always produces the same output. This preserves joins, aggregations, and downstream pipeline reproducibility.
Data lifecycle
Section titled “Data lifecycle”| Event | What happens |
|---|---|
| Training | Raw data is read via SELECT, processed in-memory, never persisted outside the ONNX model |
| Inference | Raw values enter the encoding function, latent vectors are returned. Raw values are not logged or stored. |
| Model deletion | Active model file, all archived versions, feature view, feature function, and registry entry are removed |
| App uninstall | All app-owned objects (schemas, stages, compute pools) are dropped by Snowflake |
ML/AI disclosure
Section titled “ML/AI disclosure”AIQu VEIL uses a deterministic autoencoder architecture (PyTorch training, ONNX Runtime inference). The model is trained exclusively on consumer data. No pre-trained models from external sources are used. No foundation models or generative AI components are involved.
The full ML/AI disclosure is available at VEIL ML/AI Disclosure.